A Radio-frequency Identification (RFID) chip can transmit information to a reader in response to an interrogation signal or polling request from the reader. The RFID chip can be incorporated in a tag (RFID tag) which is placed on a medical consumable item so that information can be passively captured. An RFID tag can be an active-type with its own power source, or a passive-type or battery-assisted passive type with no or limited power source. Both the passive-type and battery-assisted passive type will be referred to here as passive-type for sake of brevity. Placing an active-type RFID tag on some medical consumable items may not be feasible do to financial considerations, weight, etc. On the other hand, placing a passive-type RFID tag on medical consumable items may be more feasible; however, a power source will be needed to passively obtain information. Therefore, a device that can provide power to the RFID tag on the medical consumable item as well as obtain the information from the RFID tag would be beneficial.
Artificial Intelligence (AI) technologies such as machine learning and deep learning have become ever present due to technological advances in data storage and processing. Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. Deep learning involves neural networks inspired by our understanding of the biology of our brains all those interconnections between the neurons. But, unlike a biological brain where any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have discrete layers, connections, and directions of data propagation.